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A fine-grained sentiment classification method

A sentiment classification and fine-grained technology, applied in neural learning methods, text database clustering/classification, semantic analysis, etc., can solve problems such as poor performance and weakening network feature expression ability, and achieve improved accuracy and discrimination accuracy Improve and improve the effect of network performance

Active Publication Date: 2021-11-26
GUILIN UNIV OF ELECTRONIC TECH
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AI Technical Summary

Problems solved by technology

This makes it perform poorly in fine-grained sentiment classification tasks with multiple different targets, because the features of different sentiment words or attribute words will cancel each other out, which weakens the feature expression ability of the network

Method used

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  • A fine-grained sentiment classification method
  • A fine-grained sentiment classification method
  • A fine-grained sentiment classification method

Examples

Experimental program
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Effect test

Embodiment 1

[0082] like Figure 1 to Figure 3 As shown, a fine-grained sentiment classification method includes the following steps:

[0083] Step 1: preprocess the input sentence, and map the preprocessed sentence into a low-dimensional dense word vector in a table lookup manner;

[0084] Step 2: Input the word vector of the sentence, and the bidirectional LSTM network performs feature extraction on the word vector of the sentence to obtain the semantic feature information of the sentence

[0085] Step 3: Utilize the semantic feature information of sentences and attention mechanism to extract feature information of target attributes Using the residual connection method, the feature information of the target attribute is Information about semantic features of sentences Perform information fusion to obtain feature information feature information Perform positional encoding to obtain memory information Use location informationL o extended memory information The network me...

Embodiment 2

[0133] like Figure 2 to Figure 4 shown, a fine-grained sentiment classification system, including:

[0134] Preprocessing layer 1, used to preprocess the input sentence;

[0135] The word vector layer 2 is used to map the preprocessed sentence into a low-dimensional dense word vector by looking up a table;

[0136]The bidirectional LSTM network layer 3 is used to extract the feature of the word vector of the sentence and obtain the semantic feature information of the sentence

[0137] Memory network layer 4, used to utilize the semantic feature information of sentences and attention mechanism to extract feature information of target attributes Using the residual connection method, the feature information of the target attribute is Information about semantic features of sentences Perform information fusion to obtain feature information feature information Perform positional encoding to obtain memory information Use location informationL o extended memory info...

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Abstract

The present invention relates to a kind of fine-grained emotion classification system and method, and its method comprises the following steps: the sentence of input is preprocessed, with the mode of table look-up, is mapped to the word vector of low dimension density; Two-way LSTM network is to the word vector of sentence Perform feature extraction to obtain the semantic feature information of the sentence Use the semantic feature information of the sentence and the attention mechanism to extract the feature information of the target attribute Fuse the feature information with the semantic feature information to obtain the feature information Perform position encoding on the feature information to obtain the memory information Use the position information L o Expand memory information to get network memory information M k ; Using multi-round attention mechanism to the network memory information M of the target attribute k The emotional information of the network memory information is extracted; the emotional information is mapped to a probability vector to obtain an emotional prediction vector, and the fine-grained emotional classification results are determined based on the emotional prediction vector. Compared with the prior art, the present invention can improve network performance and improve the accuracy of fine-grained emotion classification.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, in particular, to a fine-grained emotion classification method based on target information fusion memory network. Background technique [0002] In recent years, with the rapid development of Internet technology, social media and e-commerce platforms have emerged. More and more users evaluate specific products, events, etc. on the Internet, which makes the scale of online commentary texts grow rapidly. Sentiment analysis, also known as opinion mining, is a research field that analyzes people's subjective feelings about products, services, organizations, individuals, events, topics and their attributes, such as opinions, emotions, evaluations, opinions, attitudes and other subjective feelings. Text sentiment analysis has great practical value and research value. For example, identifying sentiment information of specific commodity attributes from commodity review data ca...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/35G06F40/30G06K9/62G06N3/04G06N3/08
CPCG06F16/35G06N3/08G06N3/044G06N3/045G06F18/2411
Inventor 蔡晓东彭军
Owner GUILIN UNIV OF ELECTRONIC TECH
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